Cigarette smoking is the leading cause of morbidity and mortality in the United States and is the leading cause of lung cancer. The majority of individuals who smoke indicate they want to quit, and although half of all smokers make a quit attempt every year, only 6% successfully do so. Lower education levels, poverty, and unemployment are potent predictors of greater difficulty quitting smoking. Additionally, SES-related tobacco disparities have increased over the last several decades. Increased knowledge regarding those factors that allow individuals to be successful in staying quit is key to eliminatig smoking-related cancers and illnesses in these populations. One promising factor related to increased cessation success is mindfulness, which is the ability to attend to the present moment with purpose (e.g., not on autopilot) and without labeling thoughts/emotions/sensations as good or bad (e.g., an attitude of nonjudgment). Mindfulness is associated with increased cessation success and with key mechanisms influencing cessation such as affect and self-regulatory capacity. However, there is a distinct lack of data on real-time, real-world measures of smoking, the underlying mechanisms associated with abstinence (particularly those that may be unique among low SES populations), and how mindfulness impacts those potential mechanisms. The objective of this application is to utilize real-time, real-world measurement approaches (i.e., ecological momentary assessment [EMA] and AutoSense) to examine the impact of mindfulness on key mechanisms underlying smoking cessation among low SES, racially/ethnically diverse smokers. Christine Vinci, Ph.D. is a postdoctoral fellow at The University of Texas MD Anderson Cancer Center and is seeking 5 years of support through the K99/R00 Pathway to Independence Award through the National Institute on Minority Health and Health Disparities, in order to examine the role of mindfulness on key mechanisms implicated in cessation among low SES, diverse populations. This proposal directly addresses the mission of NIMHD to lead scientific research to improve minority health and eliminate health disparities. Dr. Vinci's research broadly focuses on cognitive and affective mechanisms (e.g., affect regulation, expectancies, self- efficacy) implicated in the development, maintenance, and treatment of substance use disorders. More specifically, her work aims to understand the mechanisms underlying smoking and alcohol use among underserved populations, with a specific focus on mindfulness. A better understanding of the effects of mindfulness on smoking and problematic drinking, and the mechanisms through which mindfulness-based interventions impact these disorders could help in the development of new and more effective interventions. For this award, the primary research aims are twofold. First, the examination of trait and state mindfulness on momentary mechanisms gathered via advanced technology (EMA and AutoSense) among low SES, racially/ethnically diverse smokers will be conducted via secondary data analysis of existing datasets of Dr. Vinci's primary mentor, Dr. David Wetter. Second, the feasibility of providing mindfulness strategies in real-time to low SES, racially/ethnically diverse smokers attempting to quit smoking, based on objective data collected via AutoSense technology, will be examined. The first aim will take place during the K99 phase of the award, where Dr. Vinci will be provided with guidance by expert mentors in their respective fields. The training and guidance received during the K99 phase will directly inform the implementation of the second aim, which will take place during the R00 phase of the award. This project is highly innovative because 1) to date, no studies have examined these constructs via real-world, real-time data among low SES, racially/ethnically diverse smokers through combined approaches such as EMA and AutoSense, and 2) findings can directly inform treatment development to specify how mindfulness impacts underlying mechanisms, leading to the reduction of tobacco- related health disparities. Dr. Vinci's training goals include learning how to design and implement research protocols that utilize advanced technology to collect participant data (EMA and AutoSense), how to conduct advanced statistical analyses on multi-level, longitudinal data, and how to combine knowledge and strategies from diverse areas of research (i.e., models linking social determinants and SES to health outcomes, tobacco use and dependence, resiliency and mindfulness, self-regulation) to create intervention approaches to reduce health disparities. Both the training received through this award and the preliminary data Dr. Vinci will collect and analyze, will aid her in the transition to an independent clinical researcher, where she will examine relevant mechanisms via mobile technology and implement mindfulness-based strategies to reduce problematic health behaviors and improve quality of life among diverse populations.